To present the process of domain adaptation training more intuitively, we utilize the t-distribution Stochastic Neighbour Embedding (t-SNE) [1]} method to visualize the low-dimensional changes of features before and after adaptation in Fig. REF . We implement one task, namely A→W (Office-31) to perform these experiments. Obviously, before training, the spatial distribution of the source domain and target domain features is completely different. This indicates that the distribution contains no discernible intrinsic structure. Although after CDAN, most of data has been aggregated, there are still some classes indistinguishable. But after our method, the feature distribution shows a clear clustered structure. The cluster centers of the distribution are closer than before, and the degree of dispersion is more similar. This means that our method greatly improves the distribution in the feature space.
